Decoding motor intent from recorded neural signals is essential for the development of effective neural controlled prostheses. The motor intent signal provides information on the class of intended movement, such as hand opening and the degree of the intended movement (e.g., half-way or fully-opened hand). The design, development, and control of prostheses with biological signals have been extensively researched. For the purpose of control of powered prostheses, a wide variety of decoding algorithms have been developed using biological signals such as electroencephalograms (EEG), electromyogram (EMG), or neural signals detected by cortical and peripheral interfaces. The decoding algorithms differ depending on the types of biological signals recorded, the design and properties of machine tissue interfaces, the design and function of prostheses, and the extent of injury (e.g. amputation, paralysis, etc.).
For example, a transradial amputee with some residual function in wrist flexor extensor muscles can be, with surface EMG recording, fitted with a one degree-of-freedom myoelectric controlled hand. In this case, the decoding algorithm is simple; it involves the filtering of EMG signals to eliminate noise and crosstalk, followed by rectifying and low-pass filtering the resultant signal to obtain a control signal to derive motors of the prosthesis. Though this kind of myoelectric control of prostheses is widely used and its decoding method simple, it has a number of limitations. Its control is limited to one degree of movement at a time, and changes in surface EMG electrodes impedance and position alter recorded EMG signals and degrade control of the prosthesis. Also, this method of control is dependent on the availability of remainder muscles, and is therefore limited to low-level amputation. Thus, this type of surface EMG control cannot provide appropriate information to control many lost degrees of freedom.
Recently, a new surgical procedure was developed to ameliorate the problems of recording EMG from residual muscles in amputees. The procedure is known as target muscle re-innervation (TMR) (Kuiken et al. 2010). A target muscle of an amputated patient is de-nervated (its original nerves cut) then re-innervated with residual nerves of the amputated limb. With many surface EMG electrodes, the problem of high level amputation can be addressed using this surgical technique, but the surgical procedure is complicated. Also, the decoding algorithms have become more complicated. Using a linear classifier, the decoder can identify the movement class, whether it's a power grip, hand opening and closing, or other class. The prosthetic arm used in this experiment was self-actuated. The amputee is only required to intend a single motion class (e.g., hand grip), and the rest of the actuation of the prosthesis is handled by the mechatronics of the prosthesis. One limitation with this approach is that the user is not able to control the degree by which to open or close their hand or to control how fast or slow the robotic arm moves; i.e., the user does not have the ability to execute graded control of the actuation of the prosthesis.
An alternative to EMG recording is recording neural activity from either the central nervous system (CNS) or the peripheral nervous system (PNS). Recently, the number of devices to interface with CNS and PNS has seen a dramatic increase due to advances in fabrication methods. CNS interfaces provide direct access to cortical or spinal cord neurons while peripheral interfaces provide access to afferent and efferent axons signals. Many CNS interfaces and PNS interfaces have different designs and functionality. Some interfaces are not invasive, such as electroencephalography (EEG) while others are invasive like penetrating electrode arrays and longitudinal intrafascicular electrodes (LIFEs). Noninvasive electrodes tend to be less specific and record average activity from a large population of neurons while invasive electrodes tend to be more specific, recording from only a few related neurons. With the increase in neural interfaces, a variety of decoding techniques have been developed.
In a recent study, a monkey was implanted in the arm area of the primary motor cortex with a 100-electrode silicon array (Wood et al. 2004). The monkey was trained to use a two-joint planar manipulandum to control the motion of a cursor on a computer screen. Hand kinematics and neural activity were recorded to study cortical encoding of hand motion. Recorded spike data was automatically sorted in the following way: first, data dimensionality was reduced by principle component analysis (PCA); then, an expectation maximization algorithm using a mixture of Gaussians was used for classification. In another study, a monkey was implanted with a single 96-channel array in the primary motor cortex (Fraser el al. 2009). Again, the objective was to have the monkey control a cursor movement on a screen. By setting a single threshold across all channels and fitting the resultant events with a spline tuning function, a control signal was extracted from this population using a Bayesian particle-filter without the need for spike sorting. Spike sorting was shown to not be necessary for high quality neuroprosthetic control.
To record from peripheral nerves, many electrode interfaces have been devised. Some common ones are nerve CUFF, FINE, LIFE, tLIFE, and penetrating electrode arrays. The different electrodes vary in terms of their level of invasiveness and signal selectivity, detection sensitivity, and purity of signals recorded. On the peripheral side, many algorithms have been developed for decoding neural activity. A 16-channel tripolar flat interface nerve electrode (FINE), a variant of the CUFF electrode, was used to record rabbits (Wodlinger et al. 2011). The beam-forming algorithm was used to recover signals from the sciatic nerve while the distal tibial and peroneal branches were stimulated. The beam-forming algorithm is a spatial filter that is able to distinguish which branch was being stimulated and how strongly, and therefore which muscles will be active. A 100-electrode Utah Slanted Electrode Arrays (USEAs) was used to record from feline sciatic nerve (Clark et al. 2011). After spike sorting, part of the neural data was used to train an offline optimal linear regression filter to relate spike counts to ankle joint angle in one plane.
A computational study compared different types of firing rate estimation methods, time-domain features (e.g. spike-duration, zero-crossing), and spike counting combined with a linear classifier, to decode simulated neural recording from UTAH arrays (Zhou el al. 2010). Thin film intrafascicular electrodes have been used to record neural activity from animal and human amputees (Micera et al. 2010). Signals were processed by wavelet de-noising followed by a spike classification stage. After spike classification, a support vector machine classifier was used to relate motor intent to neural firing. The intended movements were used to derive a finite state machine that controls a powered prosthesis. Both of these studies provide methods for identifying a class of movement, but do not provide a means for real-time graded control of the prosthesis. It has been shown that LIFEs can be used to record from peripheral nerves in amputees and control a robotic arm in a graded fashion, but only with one degree-of-freedom (Dhillon et al. 2004, 2005).
U.S. Pat. No. 8,352,385 discloses a low power analog chip to decode neural activity from cortical neurons into motor control parameters. The chip implements multiple channels of recording and uses a set of tunable parameter linear filters. However, the decoding algorithm assumes that each channel is independent and that during the tuning phase the mean firing rate is available for each channel. U.S. Pat. No. 7,442,212 describes a method by which recorded neural activity from many electrodes during a motor task is used to calculate a cumulative total spike density function in multiple dimensions. The spike density functions are used as instructions to control a prosthetic limb. This method relies on order statistics and is therefore computationally cumbersome and requires extensive computational power. Furthermore, it only identifies classes of movement and does not provide any real-time graded motor control. U.S. Pat. No. 7,058,445 is another cortical decoding scheme to control a machine. This technique uses activity from two different phases of neural activity during an intended movement. The first phase is known as the peri-movement information while the second phase is known as the movement phase. The estimated motor activities from the two phases are combined in an “optimal way” to produce a control signal. This scheme uses a point process filter to approximate non-stationary firing-rate. The method is based on switching between minimum and maximum firing rate and does not try to track the actual firing rate; rather, it approximates the firing rate by switching between maximum and minimum rates.
Thus, a variety of decoding algorithms have been used with varying degrees of success in animal and human trials. All these decoders use an assortment of signal processing techniques (band-pass filtering, wavelet-de-noising, Kalman and Wiener filters, adaptive filtering) and machine learning techniques (support vector machines, bayesian estimation). Some decoders are complex but general while others are more specialized and simple. Some decoders are fast while others are slow. Some decoders are reliable but limited while other are versatile but less accurate. Most decoders are able to produce only categorical output with a few able to do graded control when the input signals are independent and there is no cross-talk between channels. While the majority of these decoders have been implemented in software, there are some that have been implemented in specialized signal processing hardware or custom integrated circuits.